On being a professional software developer

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Slides for BitByte conference speech. September 2013.

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On being a professional software developer

  1. 1. On Being a Professional Software Developer Anton Kirillov
  2. 2. About Author antonkirillov@ akirillov@ architect@ Ph.D. in CompSci Computer Scientist, Distributed Systems Architect and Passionate Programmer
  3. 3. Agenda What is this talk about ● Developers’ Essence ● Computer Science ● Polyglot Programming ● Evolution What is this talk not about ● Agile ● Soft Skills ● How to Sell ABC to XYZ
  4. 4. Software Developer A person concerned with facets of the software development process. Their work includes researching, designing, implementing, and testing software. A software developer may take part in design, computer programming, or software project management. They may contribute to the overview of the project on the application level rather than component-level or individual programming tasks. Wikipedia
  5. 5. Software Engineering The required techniques of effective reasoning are pretty formal, but as long as programming is done by people that don't master them, the software crisis will remain with us and will be considered an incurable disease. And you know what incurable diseases do: they invite the quacks and charlatans in, who in this case take the form of Software Engineering gurus. Dijkstra (2000) "Answers to questions from students of Software Engineering"
  6. 6. Beauty is more important in computing than anywhere else in technology because software is so complicated. Beauty is the ultimate defense against complexity. David Gelernter, “Machine Beauty: Elegance and the Heart of Technology”
  7. 7. Programming We have seen that computer programming is an art, because it applies accumulated knowledge to the world, because it requires skill and ingenuity, and especially because it produces objects of beauty. A programmer who subconsciously views himself as an artist will enjoy what he does and will do it better. D. Knuth, Computer Programming as an Art (1974)
  8. 8. Programmers’ Competency Matrix
  9. 9. What’s in the Market
  10. 10. Market Requires Tools not Skills
  11. 11. Toolset is Legacy “I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail” ― Abraham Maslow “The tools we use have a profound (and devious!) influence on our thinking habits, and, therefore, on our thinking abilities” ― Edward Dijkstra
  12. 12. Skillset
  13. 13. Math & Abstraction The effective exploitation of his powers of abstraction must be regarded as one of the most vital activities of a competent programmer. E.W. Dijkstra, The Humble Programmer(1972) Abstraction in mathematics is the process of extracting the underlying essence of a mathematical concept, removing any dependence on real world objects with which it might originally have been connected, and generalizing it so that it has wider applications or matching among other abstract descriptions of equivalent phenomena.
  14. 14. Abstraction in CompSci ● Languages: ● OOP ● Functional ● Modern Lisps ● Control flow ● Abstract Data Types ● Lambda Abstraction ● Layered Architecture
  15. 15. Abstraction Principle Each significant piece of functionality in a program should be implemented in just one place in the source code. Where similar functions are carried out by distinct pieces of code, it is generally beneficial to combine them into one by abstracting out the varying parts. Benjamin C. Pierce in Types and Programming Languages (2002)
  16. 16. Why Math ● Understanding function convexity very deeply ● Using induction to prove a recursive algorithm ● Formal correctness proofs ● DFAs, NFAs, Turing Machines, and theoretical computation in general ● It makes a lot computer science concepts easier ● Specific concepts applicable to computer science are covered arguably more deeply by mathematics ● Diversity of paradigms ● Precision and skepticism ● Math people are really smart =)
  17. 17. (Very) Applied Mathematics ● (Pure) Functions and State Machines -> Stateful/Stateless Services Models ● HOF -> DI/IoC ● Set Theory -> SQL Joins ● Probability Theory -> Load Balancing, Probabilistic Data Structures
  18. 18. Comp Sci
  19. 19. Theoretical Computer Science
  20. 20. Theoretical Computer Science
  21. 21. Applied Computer Science (AI)
  22. 22. Applied CS (Engineering)
  23. 23. Applied Computer Science (Computational & Informational)
  24. 24. Data Structures & Algorithms Use Cases
  25. 25. Why Data Structures & Algorithms ● ● ● ● ● Queues: ○ Any Queueing processes ○ Distributed Persistent Queues for Event Processing Bloom filters: ■ Used by Cassandra to check which SSTables mostly contains the key ■ Hbase also uses it to optimize the reads Trees: ○ KV Database designing ○ Creating file system (S-Tree in HDFS) ○ Suffix tree: Genomic sequencing ○ Zoology: Maintaining the structures of the entire animal & plant kingdom. ○ Social Networks : Establishing relations between users based on some key B-Trees (Binary Trees): ○ E-commerce : while accessing unique keys. B-Trees balanced multi-way search tree of order N. ○ Searching : Searching quickly for a given element Skip Lists: ● Implementation of ordered sets - Redis datastore
  26. 26. Single Example: Graphs ● Search (PageRank) ● The Facebook news feed & Facebook Graph Search. ● Google Navigation and Google Directions on top of Google Maps uses some very efficient planar graph shortest path algorithms. ● Compilers use graph traversals to find code dependencies. ● Graph coloring algorithms are used when optimizing the code for parallel uses of the CPU registers. ● CPU layout design problems are modeled as graph problems. ● Memory garbage collection strategies may use graph traversals. ● Inventory allocation in web advertising can be written as a network flow problem. ● Data replication problems frequently use minimal spanning tree algorithms to keep the bandwidth use down. ● Most big data processing pipelines involve a series of interdependent steps that can be modeled as a directed acyclic graph.
  27. 27. Programming Languages
  28. 28. Programming Languages About the use of language: it is impossible to sharpen a pencil with a blunt axe. It is equally vain to try to do it with ten blunt axes instead. E.W. Dijkstra, How do we tell truths that might hurt?(1975)
  29. 29. Why Polyglot Programming Matters ● Most interesting open-source projects are in different language than yours (Storm, Finagle) ● Lay back and relax in Turing’ Tarpit ● Concurrency Idioms (Shared Memory, Actors, STM) ● Write once - run anywhere! JVM is mature enough to host new languages (and they appear!) ● We don’t need most of GoF Patterns in FP! ● There are a complete frameworks build around lack of some features in core language (DI containers)
  30. 30. Why Polyglot Programming Matters ● Another level of abstraction ● You can borrow design ideas from other languages (e.g. I am a lambda junkie) ● Different understanding of things (address arithmetic, OOP, HOF, Monads etc.) ● Effective reasoning (map and reduce) ● Idiomatic code: ceremony vs. conciseness Abstract away from language!
  31. 31. Language Landscape is Changing FORTRAN —"the infantile disorder"—, by now nearly 20 years old, is hopelessly inadequate for whatever computer application you have in mind today: it is now too clumsy, too risky, and too expensive to use. PL/I —"the fatal disease"— belongs more to the problem set than to the solution set. It is practically impossible to teach good programming to students that have had a prior exposure to BASIC: as potential programmers they are mentally mutilated beyond hope of regeneration. The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offence. APL is a mistake, carried through to perfection. It is the language of the future for the programming techniques of the past: it creates a new generation of coding bums.
  32. 32. Code Samples
  33. 33. FizzBuzz: Java for (int i = 0; i < 100; i++){ if ((i % 15) == 0){ System.out.println("FizzBuzz"); } else if ((i % 3) == 0){ System.out.println("Fizz"); } else if ((i % 5) == 0){ System.out.println("Buzz"); } else { System.out.println(i); } }
  34. 34. FizzBuzz: Java for (int i = 0; i < 100; System.out.println(++i % 3 == 0 ? i % 5 == 0 ? "Fizzbuzz" : "Fizz" : i % 5 == 0 ? "Buzz" : i));
  35. 35. FizzBuzz: Scala (1 to 100) map { x => (x % 3, x % 5) match { case (0,0) => "FizzBuzz" case (0,_) => "Fizz" case (_,0) => "Buzz" case _ => x toString } } foreach println
  36. 36. FizzBuzz: Clojure (use '[match.core :only (match)]) (doseq [n (range 1 101)] (println (match [(mod n 3) (mod n 5)] [0 0] "FizzBuzz" [0 _] "Fizz" [_ 0] "Buzz" :else n)))
  37. 37. FizzBuzz: Enterprise Edition EnterpriseQualityCoding/FizzBuzzEnterpriseEdition LoopComponentFactory myLoopComponentFactory = new LoopComponentFactory(); LoopInitializer myLoopInitializer = myLoopComponentFactory.createLoopInitializer(); LoopCondition myLoopCondition = myLoopComponentFactory.createLoopCondition(); LoopStep myLoopStep = myLoopComponentFactory.createLoopStep(); IsEvenlyDivisibleStrategyFactory myFizzStrategyFactory = new FizzStrategyFactory(); IsEvenlyDivisibleStrategy myFizzStrategy = myFizzStrategyFactory.createIsEvenlyDivisibleStrategy(); StringPrinterFactory myFizzStringPrinterFactory = new FizzStringPrinterFactory(); StringPrinter myFizzStringPrinter = myFizzStringPrinterFactory.createStringPrinter(); IsEvenlyDivisibleStrategyFactory myBuzzStrategyFactory = new BuzzStrategyFactory(); IsEvenlyDivisibleStrategy myBuzzStrategy = myBuzzStrategyFactory.createIsEvenlyDivisibleStrategy(); StringPrinterFactory myBuzzStringPrinterFactory = new BuzzStringPrinterFactory(); StringPrinter myBuzzStringPrinter = myBuzzStringPrinterFactory.createStringPrinter(); IsEvenlyDivisibleStrategyFactory myNoFizzNoBuzzStrategyFactory = new NoFizzNoBuzzStrategyFactory(); …..
  38. 38. FizzBuzz: Enterprise Edition EnterpriseQualityCoding/FizzBuzzEnterpriseEdition
  39. 39. Evolve!
  40. 40. Evolve! ● Wiki that! ● Build a robot! ● Attack a book! ● Compete! ● Toy projects! ● Open-source! ● Coursera? ● Hire people smarter than You!
  41. 41. Enjoy the Community! ● Attend conferences, check! ● Attend meetups in Your city ● Share Your knowledge, sync with community! Ads =)
  42. 42. Recap ● Computer programming is the core competency ● Don’t depend on market ● Abstraction as main tool ● Choose field, not framework ● Be polyglot ● Don’t fear to explore ● Evolve
  43. 43. First they ignore you, then they laugh at you, then they fight you, then you win Mahatma Gandhi
  44. 44. {} thisRoom getPeople foreach( person => { shakeHand(person) thanks(person) } > ~questions?

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